Explainable Screening Algorithms for Oncology Images in Resource-Limited Facilities: An Audit-Ready Methods Framework
DOI:
https://doi.org/10.66687/m5rwyj65Keywords:
Artificial Intelligence, Machine Learning, Medical Informatics, Decision Support Systems, Clinical, Reproducibility of ResultsAbstract
Pathology-image classifiers trained on curated slides may fail when tissue preparation, scanner optics and image quality vary between laboratories. The difficulty is particularly visible when a system is transferred to a new setting. The central research objective is to determine whether interpretable image models preserve diagnostic reliability across acquisition settings. Previous work provides context for sampling, input validation and study boundaries [3–7]. To answer the question, the study proposes a cross-site image-validation design using stratified slide collections and blinded review of model explanation maps. Enterprise-storage sources are used to specify constraints on data availability and provenance [1], with complementary infrastructure evidence defining practical recovery safeguards [2]. These citations are not treated as clinical effectiveness evidence. Under matched evaluation conditions, the protocol compares a conventional image classifier, an explanation-augmented classifier and readers reviewing the same slides. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The audit-focused extension preserves processing logs, model decisions and exception handling for independent review. Performance will be judged using site-specific sensitivity, calibration, false-negative rates and agreement between highlighted regions and pathology annotations. Interpretation will account for patient-level separation, stain perturbations, class imbalance and disagreements between expert reviewers. Uncertain outcomes would be adjudicated with a predefined rubric, rather than silently recoded as successes. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].
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